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family_materialize

Build a complete variant family from one design table, generating each part by recipe and assigning item and sequential part numbers in instances or configurations modes.

Instructions

Materialize a whole variant family from ONE design table (issue #138, B1) — "make all the gears" becomes a table, not a loop. For each row, in table order, builds the part with its recipe (A1, #136) and allocates one item + one sequential part number (C1, #140). Subsumes standard-part catalogs: a bearing catalog is a family table keyed by designation, sourced from the ISO corpus.

Two modes (both supported): "instances" (each variant its own released file + part number) and "configurations" (variants share one artifact). Builds into the active document. Returns {schema, family, recipe, mode, key, count, rows, registry}.

table: path to the family table (.csv or .json). registry: optional items.json path (created if absent, written back). mode: optional override of the table's mode (instances|configurations).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
tableYes
registryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden and does well: it discloses that it builds into the active document, creates items and sequential part numbers, creates the registry file if absent and writes it back, and returns a specific object shape. It doesn't cover failure modes or side effects like what happens on duplicate keys, but the mutation and allocation behavior is well disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The content is valuable but front-loaded awkwardly: the parenthetical issue/PR references (issue #138, B1, A1 #136, C1 #140) and the ISO corpus tangent add noise. The parameter list at the end is well-structured, but the prose block is denser than needed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 3-param mutation tool with no annotations and no output schema, the description is nearly complete: it covers inputs, modes, side effects (registry write-back, builds into active document), and the return shape. The main gap is the expected table format/columns, which an agent would need to supply valid input.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It documents all three parameters: table (path to .csv or .json), registry (optional items.json path, created if absent, written back), and mode (optional override of table's mode with instances|configurations enum). This is strong compensation, though it doesn't specify the table's required columns/schema, which matters for a 'family table'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Materialize a whole variant family from ONE design table', with a vivid framing ('make all the gears becomes a table, not a loop'). It distinguishes itself from siblings like family_validate and recipe by describing the batch-build-and-allocate behavior, though it doesn't explicitly name which sibling to use instead for validation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explains the two modes clearly and when each applies (instances vs configurations), and notes it subsumes standard-part catalogs. It implies usage context strongly (building a family from a table) but doesn't explicitly state when NOT to use it versus family_validate or recipe_validate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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